Smart Fabrics with Integrated Pathogen Detection, Repellency, and Antimicrobial Properties for Healthcare Applications

Noor Abu Jarad,Akansha Prasad,Sara Rahmani,Fereshteh Bayat,Mathura Thirugnanasampanthar,Zeinab Hosseinidoust,Leyla Soleymani,Tohid F. Didar

ADVANCED FUNCTIONAL MATERIALS（2024）

McMaster Univ

Cited 1|Views1

Abstract

Healthcare textiles serve as key reservoirs for pathogen proliferation, demanding an urgent call for innovative interventions. Here, a new class of Smart Fabrics (SF) is introduced with integrated “Repel, Kill, and Detect” functionalities, achieved through a blend of hierarchically structured microparticles, modified nanoparticles, and an acidity‐responsive sensor. SF exhibit remarkable resilience against aerosol and droplet‐based pathogen transmission, showcasing a reduction exceeding 99.90% compared to uncoated fabrics across various drug‐resistant bacteria, Candida albicans, and Phi6 virus. Experiments involving bodily fluids from healthy and infected individuals reveal a significant reduction of 99.88% and 99.79% in clinical urine and feces samples, respectively, compared to uncoated fabrics. The SF's colorimetric detection capability coupled with machine learning (96.67% accuracy) ensures reliable pathogen identification, facilitating accurate differentiation between healthy and infected urine and fecal contaminated samples. SF holds promise for revolutionizing infection prevention and control in healthcare facilities, providing protection through early contamination detection.

Translated text

Key words

anti-microbial fabrics,biofilms,hierarchical structures,omniphobic coating,optical sensing,smart fabrics,smart textile

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper